Genomic surveillance of SARS-CoV-2 variants using pooled WGS

Abstract

This study presents the development and validation of a genomic surveillance strategy using Whole Genome Sequencing (WGS) on normalized pooled samples to detect and monitor SARS-CoV-2 variants. A bioinformatics pipeline was designed specifically for analyzing pooled WGS data and was validated using simulated datasets, pooled samples of reference materials, and pooled clinical samples collected during key periods of the Delta and Omicron variant emergence. The approach was evaluated for its accuracy in estimating variant abundance at both the Phylogenetic Assignment of Named Global Outbreak (PANGO) lineage level and the World Health Organization (WHO) variant level. From the simulation datasets, the method achieved an overall sensitivity of 99.1% and a positive predictive value (PPV) of 99.9% for detecting SARS-CoV-2 variants at the WHO variant level. At the PANGO lineage level, it achieved an overall sensitivity of 82.8% and a PPV of 77.4% when a predicted lineage was considered accurate if it shared more than 90% of markers with any true lineage present in the pooled sample. The accuracy of variant abundance estimation was further validated using pooled samples of reference materials. Analysis of pooled clinical samples showed results consistent with national epidemiological trends, particularly during the emergence of the Delta and Omicron variants in Korea. This pooled WGS-based genomic surveillance strategy offers a scalable and economical solution for monitoring SARS-CoV-2 variants, providing public health authorities with a valuable tool for tracking pandemic dynamics and enabling timely responses.